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  <div class="section" id="tensor">
<h1>Tensor<a class="headerlink" href="#tensor" title="Permalink to this headline">¶</a></h1>
<dl class="type">
<dt id="org.pytorch.Tensor">
public abstract class <code class="sig-name descname">Tensor</code><a class="headerlink" href="#org.pytorch.Tensor" title="Permalink to this definition">¶</a></dt>
<dd><p>Representation of a Tensor. Behavior is similar to PyTorch’s tensor objects.</p>
<p>Most tensors will be constructed as <code class="docutils literal notranslate"><span class="pre">Tensor.fromBlob(data,</span> <span class="pre">shape)</span></code>, where <code class="docutils literal notranslate"><span class="pre">data</span></code> can be an array or a direct <a class="reference external" href="http://docs.oracle.com/javase/8/docs/api/java/nio/Buffer.html" title="java.nio.Buffer"><code class="xref java java-ref docutils literal notranslate"><span class="pre">Buffer</span></code></a> (of the proper subclass). Helper methods are provided to allocate buffers properly.</p>
<p>To access Tensor data, see <a class="reference internal" href="#org.pytorch.Tensor.dtype()" title="org.pytorch.Tensor.dtype()"><code class="xref java java-ref docutils literal notranslate"><span class="pre">dtype()</span></code></a>, <a class="reference internal" href="#org.pytorch.Tensor.shape()" title="org.pytorch.Tensor.shape()"><code class="xref java java-ref docutils literal notranslate"><span class="pre">shape()</span></code></a>, and various <code class="docutils literal notranslate"><span class="pre">getDataAs*</span></code> methods.</p>
<p>When constructing <code class="docutils literal notranslate"><span class="pre">Tensor</span></code> objects with <code class="docutils literal notranslate"><span class="pre">data</span></code> as an array, it is not specified whether this data is is copied or retained as a reference so it is recommended not to modify it after constructing. <code class="docutils literal notranslate"><span class="pre">data</span></code> passed as a <a class="reference external" href="http://docs.oracle.com/javase/8/docs/api/java/nio/Buffer.html" title="java.nio.Buffer"><code class="xref java java-ref docutils literal notranslate"><span class="pre">Buffer</span></code></a> is not copied, so it can be modified between <a class="reference internal" href="Module.html#org.pytorch.Module" title="org.pytorch.Module"><code class="xref java java-ref docutils literal notranslate"><span class="pre">Module</span></code></a> calls to avoid reallocation. Data retrieved from <code class="docutils literal notranslate"><span class="pre">Tensor</span></code> objects may be copied or may be a reference to the <code class="docutils literal notranslate"><span class="pre">Tensor</span></code>’s internal data buffer. <code class="docutils literal notranslate"><span class="pre">shape</span></code> is always copied.</p>
</dd></dl>

<div class="section" id="methods">
<h2>Methods<a class="headerlink" href="#methods" title="Permalink to this headline">¶</a></h2>
<div class="section" id="allocatebytebuffer">
<h3>allocateByteBuffer<a class="headerlink" href="#allocatebytebuffer" title="Permalink to this headline">¶</a></h3>
<dl class="method">
<dt id="org.pytorch.Tensor.allocateByteBuffer(int)">
public static <a class="reference external" href="http://docs.oracle.com/javase/8/docs/api/java/nio/ByteBuffer.html" title="java.nio.ByteBuffer">ByteBuffer</a> <code class="sig-name descname">allocateByteBuffer</code><span class="sig-paren">(</span>int<em> numElements</em><span class="sig-paren">)</span><a class="headerlink" href="#org.pytorch.Tensor.allocateByteBuffer(int)" title="Permalink to this definition">¶</a></dt>
<dd><p>Allocates a new direct <a class="reference external" href="http://docs.oracle.com/javase/8/docs/api/java/nio/ByteBuffer.html" title="java.nio.ByteBuffer"><code class="xref java java-ref docutils literal notranslate"><span class="pre">java.nio.ByteBuffer</span></code></a> with native byte order with specified capacity that can be used in <a class="reference internal" href="#org.pytorch.Tensor.fromBlob(DoubleBuffer, long[])" title="org.pytorch.Tensor.fromBlob(DoubleBuffer, long[])"><code class="xref java java-ref docutils literal notranslate"><span class="pre">Tensor.fromBlob(ByteBuffer,long[])</span></code></a>, <a class="reference internal" href="#org.pytorch.Tensor.fromBlobUnsigned(ByteBuffer, long[])" title="org.pytorch.Tensor.fromBlobUnsigned(ByteBuffer, long[])"><code class="xref java java-ref docutils literal notranslate"><span class="pre">Tensor.fromBlobUnsigned(ByteBuffer,long[])</span></code></a>.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>numElements</strong> – capacity (number of elements) of result buffer.</p></li>
</ul>
</dd>
</dl>
</dd></dl>

</div>
<div class="section" id="allocatedoublebuffer">
<h3>allocateDoubleBuffer<a class="headerlink" href="#allocatedoublebuffer" title="Permalink to this headline">¶</a></h3>
<dl class="method">
<dt id="org.pytorch.Tensor.allocateDoubleBuffer(int)">
public static <a class="reference external" href="http://docs.oracle.com/javase/8/docs/api/java/nio/DoubleBuffer.html" title="java.nio.DoubleBuffer">DoubleBuffer</a> <code class="sig-name descname">allocateDoubleBuffer</code><span class="sig-paren">(</span>int<em> numElements</em><span class="sig-paren">)</span><a class="headerlink" href="#org.pytorch.Tensor.allocateDoubleBuffer(int)" title="Permalink to this definition">¶</a></dt>
<dd><p>Allocates a new direct <a class="reference external" href="http://docs.oracle.com/javase/8/docs/api/java/nio/DoubleBuffer.html" title="java.nio.DoubleBuffer"><code class="xref java java-ref docutils literal notranslate"><span class="pre">java.nio.DoubleBuffer</span></code></a> with native byte order with specified capacity that can be used in <a class="reference internal" href="#org.pytorch.Tensor.fromBlob(DoubleBuffer, long[])" title="org.pytorch.Tensor.fromBlob(DoubleBuffer, long[])"><code class="xref java java-ref docutils literal notranslate"><span class="pre">Tensor.fromBlob(DoubleBuffer,long[])</span></code></a>.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>numElements</strong> – capacity (number of elements) of result buffer.</p></li>
</ul>
</dd>
</dl>
</dd></dl>

</div>
<div class="section" id="allocatefloatbuffer">
<h3>allocateFloatBuffer<a class="headerlink" href="#allocatefloatbuffer" title="Permalink to this headline">¶</a></h3>
<dl class="method">
<dt id="org.pytorch.Tensor.allocateFloatBuffer(int)">
public static <a class="reference external" href="http://docs.oracle.com/javase/8/docs/api/java/nio/FloatBuffer.html" title="java.nio.FloatBuffer">FloatBuffer</a> <code class="sig-name descname">allocateFloatBuffer</code><span class="sig-paren">(</span>int<em> numElements</em><span class="sig-paren">)</span><a class="headerlink" href="#org.pytorch.Tensor.allocateFloatBuffer(int)" title="Permalink to this definition">¶</a></dt>
<dd><p>Allocates a new direct <a class="reference external" href="http://docs.oracle.com/javase/8/docs/api/java/nio/FloatBuffer.html" title="java.nio.FloatBuffer"><code class="xref java java-ref docutils literal notranslate"><span class="pre">java.nio.FloatBuffer</span></code></a> with native byte order with specified capacity that can be used in <a class="reference internal" href="#org.pytorch.Tensor.fromBlob(DoubleBuffer, long[])" title="org.pytorch.Tensor.fromBlob(DoubleBuffer, long[])"><code class="xref java java-ref docutils literal notranslate"><span class="pre">Tensor.fromBlob(FloatBuffer,long[])</span></code></a>.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>numElements</strong> – capacity (number of elements) of result buffer.</p></li>
</ul>
</dd>
</dl>
</dd></dl>

</div>
<div class="section" id="allocateintbuffer">
<h3>allocateIntBuffer<a class="headerlink" href="#allocateintbuffer" title="Permalink to this headline">¶</a></h3>
<dl class="method">
<dt id="org.pytorch.Tensor.allocateIntBuffer(int)">
public static <a class="reference external" href="http://docs.oracle.com/javase/8/docs/api/java/nio/IntBuffer.html" title="java.nio.IntBuffer">IntBuffer</a> <code class="sig-name descname">allocateIntBuffer</code><span class="sig-paren">(</span>int<em> numElements</em><span class="sig-paren">)</span><a class="headerlink" href="#org.pytorch.Tensor.allocateIntBuffer(int)" title="Permalink to this definition">¶</a></dt>
<dd><p>Allocates a new direct <a class="reference external" href="http://docs.oracle.com/javase/8/docs/api/java/nio/IntBuffer.html" title="java.nio.IntBuffer"><code class="xref java java-ref docutils literal notranslate"><span class="pre">java.nio.IntBuffer</span></code></a> with native byte order with specified capacity that can be used in <a class="reference internal" href="#org.pytorch.Tensor.fromBlob(DoubleBuffer, long[])" title="org.pytorch.Tensor.fromBlob(DoubleBuffer, long[])"><code class="xref java java-ref docutils literal notranslate"><span class="pre">Tensor.fromBlob(IntBuffer,long[])</span></code></a>.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>numElements</strong> – capacity (number of elements) of result buffer.</p></li>
</ul>
</dd>
</dl>
</dd></dl>

</div>
<div class="section" id="allocatelongbuffer">
<h3>allocateLongBuffer<a class="headerlink" href="#allocatelongbuffer" title="Permalink to this headline">¶</a></h3>
<dl class="method">
<dt id="org.pytorch.Tensor.allocateLongBuffer(int)">
public static <a class="reference external" href="http://docs.oracle.com/javase/8/docs/api/java/nio/LongBuffer.html" title="java.nio.LongBuffer">LongBuffer</a> <code class="sig-name descname">allocateLongBuffer</code><span class="sig-paren">(</span>int<em> numElements</em><span class="sig-paren">)</span><a class="headerlink" href="#org.pytorch.Tensor.allocateLongBuffer(int)" title="Permalink to this definition">¶</a></dt>
<dd><p>Allocates a new direct <a class="reference external" href="http://docs.oracle.com/javase/8/docs/api/java/nio/LongBuffer.html" title="java.nio.LongBuffer"><code class="xref java java-ref docutils literal notranslate"><span class="pre">java.nio.LongBuffer</span></code></a> with native byte order with specified capacity that can be used in <a class="reference internal" href="#org.pytorch.Tensor.fromBlob(DoubleBuffer, long[])" title="org.pytorch.Tensor.fromBlob(DoubleBuffer, long[])"><code class="xref java java-ref docutils literal notranslate"><span class="pre">Tensor.fromBlob(LongBuffer,long[])</span></code></a>.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>numElements</strong> – capacity (number of elements) of result buffer.</p></li>
</ul>
</dd>
</dl>
</dd></dl>

</div>
<div class="section" id="dtype">
<h3>dtype<a class="headerlink" href="#dtype" title="Permalink to this headline">¶</a></h3>
<dl class="method">
<dt id="org.pytorch.Tensor.dtype()">
public abstract <a class="reference internal" href="DType.html#org.pytorch.DType" title="org.pytorch.DType">DType</a> <code class="sig-name descname">dtype</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#org.pytorch.Tensor.dtype()" title="Permalink to this definition">¶</a></dt>
<dd><dl class="field-list simple">
<dt class="field-odd">Returns</dt>
<dd class="field-odd"><p>data type of this tensor.</p>
</dd>
</dl>
</dd></dl>

</div>
<div class="section" id="dtypejnicode">
<h3>dtypeJniCode<a class="headerlink" href="#dtypejnicode" title="Permalink to this headline">¶</a></h3>
<dl class="method">
<dt id="org.pytorch.Tensor.dtypeJniCode()">
 int <code class="sig-name descname">dtypeJniCode</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#org.pytorch.Tensor.dtypeJniCode()" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>

</div>
<div class="section" id="fromblob">
<h3>fromBlob<a class="headerlink" href="#fromblob" title="Permalink to this headline">¶</a></h3>
<dl class="method">
<dt id="org.pytorch.Tensor.fromBlob(byte[], long[])">
public static <a class="reference internal" href="#org.pytorch.Tensor" title="org.pytorch.Tensor">Tensor</a> <code class="sig-name descname">fromBlob</code><span class="sig-paren">(</span>byte[]<em> data</em>, long[]<em> shape</em><span class="sig-paren">)</span><a class="headerlink" href="#org.pytorch.Tensor.fromBlob(byte[], long[])" title="Permalink to this definition">¶</a></dt>
<dd><p>Creates a new Tensor instance with dtype torch.int8 with specified shape and data as array of bytes.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>data</strong> – Tensor elements</p></li>
<li><p><strong>shape</strong> – Tensor shape</p></li>
</ul>
</dd>
</dl>
</dd></dl>

</div>
<div class="section" id="id1">
<h3>fromBlob<a class="headerlink" href="#id1" title="Permalink to this headline">¶</a></h3>
<dl class="method">
<dt id="org.pytorch.Tensor.fromBlob(int[], long[])">
public static <a class="reference internal" href="#org.pytorch.Tensor" title="org.pytorch.Tensor">Tensor</a> <code class="sig-name descname">fromBlob</code><span class="sig-paren">(</span>int[]<em> data</em>, long[]<em> shape</em><span class="sig-paren">)</span><a class="headerlink" href="#org.pytorch.Tensor.fromBlob(int[], long[])" title="Permalink to this definition">¶</a></dt>
<dd><p>Creates a new Tensor instance with dtype torch.int32 with specified shape and data as array of ints.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>data</strong> – Tensor elements</p></li>
<li><p><strong>shape</strong> – Tensor shape</p></li>
</ul>
</dd>
</dl>
</dd></dl>

</div>
<div class="section" id="id2">
<h3>fromBlob<a class="headerlink" href="#id2" title="Permalink to this headline">¶</a></h3>
<dl class="method">
<dt id="org.pytorch.Tensor.fromBlob(float[], long[])">
public static <a class="reference internal" href="#org.pytorch.Tensor" title="org.pytorch.Tensor">Tensor</a> <code class="sig-name descname">fromBlob</code><span class="sig-paren">(</span>float[]<em> data</em>, long[]<em> shape</em><span class="sig-paren">)</span><a class="headerlink" href="#org.pytorch.Tensor.fromBlob(float[], long[])" title="Permalink to this definition">¶</a></dt>
<dd><p>Creates a new Tensor instance with dtype torch.float32 with specified shape and data as array of floats.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>data</strong> – Tensor elements</p></li>
<li><p><strong>shape</strong> – Tensor shape</p></li>
</ul>
</dd>
</dl>
</dd></dl>

</div>
<div class="section" id="id3">
<h3>fromBlob<a class="headerlink" href="#id3" title="Permalink to this headline">¶</a></h3>
<dl class="method">
<dt id="org.pytorch.Tensor.fromBlob(long[], long[])">
public static <a class="reference internal" href="#org.pytorch.Tensor" title="org.pytorch.Tensor">Tensor</a> <code class="sig-name descname">fromBlob</code><span class="sig-paren">(</span>long[]<em> data</em>, long[]<em> shape</em><span class="sig-paren">)</span><a class="headerlink" href="#org.pytorch.Tensor.fromBlob(long[], long[])" title="Permalink to this definition">¶</a></dt>
<dd><p>Creates a new Tensor instance with dtype torch.int64 with specified shape and data as array of longs.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>data</strong> – Tensor elements</p></li>
<li><p><strong>shape</strong> – Tensor shape</p></li>
</ul>
</dd>
</dl>
</dd></dl>

</div>
<div class="section" id="id4">
<h3>fromBlob<a class="headerlink" href="#id4" title="Permalink to this headline">¶</a></h3>
<dl class="method">
<dt id="org.pytorch.Tensor.fromBlob(long[], double[])">
public static <a class="reference internal" href="#org.pytorch.Tensor" title="org.pytorch.Tensor">Tensor</a> <code class="sig-name descname">fromBlob</code><span class="sig-paren">(</span>long[]<em> shape</em>, double[]<em> data</em><span class="sig-paren">)</span><a class="headerlink" href="#org.pytorch.Tensor.fromBlob(long[], double[])" title="Permalink to this definition">¶</a></dt>
<dd><p>Creates a new Tensor instance with dtype torch.float64 with specified shape and data as array of doubles.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>shape</strong> – Tensor shape</p></li>
<li><p><strong>data</strong> – Tensor elements</p></li>
</ul>
</dd>
</dl>
</dd></dl>

</div>
<div class="section" id="id5">
<h3>fromBlob<a class="headerlink" href="#id5" title="Permalink to this headline">¶</a></h3>
<dl class="method">
<dt id="org.pytorch.Tensor.fromBlob(ByteBuffer, long[])">
public static <a class="reference internal" href="#org.pytorch.Tensor" title="org.pytorch.Tensor">Tensor</a> <code class="sig-name descname">fromBlob</code><span class="sig-paren">(</span><a class="reference external" href="http://docs.oracle.com/javase/8/docs/api/java/nio/ByteBuffer.html" title="java.nio.ByteBuffer">ByteBuffer</a><em> data</em>, long[]<em> shape</em><span class="sig-paren">)</span><a class="headerlink" href="#org.pytorch.Tensor.fromBlob(ByteBuffer, long[])" title="Permalink to this definition">¶</a></dt>
<dd><p>Creates a new Tensor instance with dtype torch.int8 with specified shape and data.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>data</strong> – Direct buffer with native byte order that contains <code class="docutils literal notranslate"><span class="pre">Tensor.numel(shape)</span></code> elements. The buffer is used directly without copying, and changes to its content will change the tensor.</p></li>
<li><p><strong>shape</strong> – Tensor shape</p></li>
</ul>
</dd>
</dl>
</dd></dl>

</div>
<div class="section" id="id6">
<h3>fromBlob<a class="headerlink" href="#id6" title="Permalink to this headline">¶</a></h3>
<dl class="method">
<dt id="org.pytorch.Tensor.fromBlob(IntBuffer, long[])">
public static <a class="reference internal" href="#org.pytorch.Tensor" title="org.pytorch.Tensor">Tensor</a> <code class="sig-name descname">fromBlob</code><span class="sig-paren">(</span><a class="reference external" href="http://docs.oracle.com/javase/8/docs/api/java/nio/IntBuffer.html" title="java.nio.IntBuffer">IntBuffer</a><em> data</em>, long[]<em> shape</em><span class="sig-paren">)</span><a class="headerlink" href="#org.pytorch.Tensor.fromBlob(IntBuffer, long[])" title="Permalink to this definition">¶</a></dt>
<dd><p>Creates a new Tensor instance with dtype torch.int32 with specified shape and data.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>data</strong> – Direct buffer with native byte order that contains <code class="docutils literal notranslate"><span class="pre">Tensor.numel(shape)</span></code> elements. The buffer is used directly without copying, and changes to its content will change the tensor.</p></li>
<li><p><strong>shape</strong> – Tensor shape</p></li>
</ul>
</dd>
</dl>
</dd></dl>

</div>
<div class="section" id="id7">
<h3>fromBlob<a class="headerlink" href="#id7" title="Permalink to this headline">¶</a></h3>
<dl class="method">
<dt id="org.pytorch.Tensor.fromBlob(FloatBuffer, long[])">
public static <a class="reference internal" href="#org.pytorch.Tensor" title="org.pytorch.Tensor">Tensor</a> <code class="sig-name descname">fromBlob</code><span class="sig-paren">(</span><a class="reference external" href="http://docs.oracle.com/javase/8/docs/api/java/nio/FloatBuffer.html" title="java.nio.FloatBuffer">FloatBuffer</a><em> data</em>, long[]<em> shape</em><span class="sig-paren">)</span><a class="headerlink" href="#org.pytorch.Tensor.fromBlob(FloatBuffer, long[])" title="Permalink to this definition">¶</a></dt>
<dd><p>Creates a new Tensor instance with dtype torch.float32 with specified shape and data.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>data</strong> – Direct buffer with native byte order that contains <code class="docutils literal notranslate"><span class="pre">Tensor.numel(shape)</span></code> elements. The buffer is used directly without copying, and changes to its content will change the tensor.</p></li>
<li><p><strong>shape</strong> – Tensor shape</p></li>
</ul>
</dd>
</dl>
</dd></dl>

</div>
<div class="section" id="id8">
<h3>fromBlob<a class="headerlink" href="#id8" title="Permalink to this headline">¶</a></h3>
<dl class="method">
<dt id="org.pytorch.Tensor.fromBlob(LongBuffer, long[])">
public static <a class="reference internal" href="#org.pytorch.Tensor" title="org.pytorch.Tensor">Tensor</a> <code class="sig-name descname">fromBlob</code><span class="sig-paren">(</span><a class="reference external" href="http://docs.oracle.com/javase/8/docs/api/java/nio/LongBuffer.html" title="java.nio.LongBuffer">LongBuffer</a><em> data</em>, long[]<em> shape</em><span class="sig-paren">)</span><a class="headerlink" href="#org.pytorch.Tensor.fromBlob(LongBuffer, long[])" title="Permalink to this definition">¶</a></dt>
<dd><p>Creates a new Tensor instance with dtype torch.int64 with specified shape and data.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>data</strong> – Direct buffer with native byte order that contains <code class="docutils literal notranslate"><span class="pre">Tensor.numel(shape)</span></code> elements. The buffer is used directly without copying, and changes to its content will change the tensor.</p></li>
<li><p><strong>shape</strong> – Tensor shape</p></li>
</ul>
</dd>
</dl>
</dd></dl>

</div>
<div class="section" id="id9">
<h3>fromBlob<a class="headerlink" href="#id9" title="Permalink to this headline">¶</a></h3>
<dl class="method">
<dt id="org.pytorch.Tensor.fromBlob(DoubleBuffer, long[])">
public static <a class="reference internal" href="#org.pytorch.Tensor" title="org.pytorch.Tensor">Tensor</a> <code class="sig-name descname">fromBlob</code><span class="sig-paren">(</span><a class="reference external" href="http://docs.oracle.com/javase/8/docs/api/java/nio/DoubleBuffer.html" title="java.nio.DoubleBuffer">DoubleBuffer</a><em> data</em>, long[]<em> shape</em><span class="sig-paren">)</span><a class="headerlink" href="#org.pytorch.Tensor.fromBlob(DoubleBuffer, long[])" title="Permalink to this definition">¶</a></dt>
<dd><p>Creates a new Tensor instance with dtype torch.float64 with specified shape and data.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>data</strong> – Direct buffer with native byte order that contains <code class="docutils literal notranslate"><span class="pre">Tensor.numel(shape)</span></code> elements. The buffer is used directly without copying, and changes to its content will change the tensor.</p></li>
<li><p><strong>shape</strong> – Tensor shape</p></li>
</ul>
</dd>
</dl>
</dd></dl>

</div>
<div class="section" id="fromblobunsigned">
<h3>fromBlobUnsigned<a class="headerlink" href="#fromblobunsigned" title="Permalink to this headline">¶</a></h3>
<dl class="method">
<dt id="org.pytorch.Tensor.fromBlobUnsigned(byte[], long[])">
public static <a class="reference internal" href="#org.pytorch.Tensor" title="org.pytorch.Tensor">Tensor</a> <code class="sig-name descname">fromBlobUnsigned</code><span class="sig-paren">(</span>byte[]<em> data</em>, long[]<em> shape</em><span class="sig-paren">)</span><a class="headerlink" href="#org.pytorch.Tensor.fromBlobUnsigned(byte[], long[])" title="Permalink to this definition">¶</a></dt>
<dd><p>Creates a new Tensor instance with dtype torch.uint8 with specified shape and data as array of bytes.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>data</strong> – Tensor elements</p></li>
<li><p><strong>shape</strong> – Tensor shape</p></li>
</ul>
</dd>
</dl>
</dd></dl>

</div>
<div class="section" id="id10">
<h3>fromBlobUnsigned<a class="headerlink" href="#id10" title="Permalink to this headline">¶</a></h3>
<dl class="method">
<dt id="org.pytorch.Tensor.fromBlobUnsigned(ByteBuffer, long[])">
public static <a class="reference internal" href="#org.pytorch.Tensor" title="org.pytorch.Tensor">Tensor</a> <code class="sig-name descname">fromBlobUnsigned</code><span class="sig-paren">(</span><a class="reference external" href="http://docs.oracle.com/javase/8/docs/api/java/nio/ByteBuffer.html" title="java.nio.ByteBuffer">ByteBuffer</a><em> data</em>, long[]<em> shape</em><span class="sig-paren">)</span><a class="headerlink" href="#org.pytorch.Tensor.fromBlobUnsigned(ByteBuffer, long[])" title="Permalink to this definition">¶</a></dt>
<dd><p>Creates a new Tensor instance with dtype torch.uint8 with specified shape and data.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>data</strong> – Direct buffer with native byte order that contains <code class="docutils literal notranslate"><span class="pre">Tensor.numel(shape)</span></code> elements. The buffer is used directly without copying, and changes to its content will change the tensor.</p></li>
<li><p><strong>shape</strong> – Tensor shape</p></li>
</ul>
</dd>
</dl>
</dd></dl>

</div>
<div class="section" id="getdataasbytearray">
<h3>getDataAsByteArray<a class="headerlink" href="#getdataasbytearray" title="Permalink to this headline">¶</a></h3>
<dl class="method">
<dt id="org.pytorch.Tensor.getDataAsByteArray()">
public byte[] <code class="sig-name descname">getDataAsByteArray</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#org.pytorch.Tensor.getDataAsByteArray()" title="Permalink to this definition">¶</a></dt>
<dd><dl class="field-list simple">
<dt class="field-odd">Throws</dt>
<dd class="field-odd"><ul class="simple">
<li><p><a class="reference external" href="http://docs.oracle.com/javase/8/docs/api/java/lang/IllegalStateException.html" title="java.lang.IllegalStateException"><strong>IllegalStateException</strong></a> – if it is called for a non-int8 tensor.</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>a Java byte array that contains the tensor data. This may be a copy or reference.</p>
</dd>
</dl>
</dd></dl>

</div>
<div class="section" id="getdataasdoublearray">
<h3>getDataAsDoubleArray<a class="headerlink" href="#getdataasdoublearray" title="Permalink to this headline">¶</a></h3>
<dl class="method">
<dt id="org.pytorch.Tensor.getDataAsDoubleArray()">
public double[] <code class="sig-name descname">getDataAsDoubleArray</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#org.pytorch.Tensor.getDataAsDoubleArray()" title="Permalink to this definition">¶</a></dt>
<dd><dl class="field-list simple">
<dt class="field-odd">Throws</dt>
<dd class="field-odd"><ul class="simple">
<li><p><a class="reference external" href="http://docs.oracle.com/javase/8/docs/api/java/lang/IllegalStateException.html" title="java.lang.IllegalStateException"><strong>IllegalStateException</strong></a> – if it is called for a non-float64 tensor.</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>a Java double array that contains the tensor data. This may be a copy or reference.</p>
</dd>
</dl>
</dd></dl>

</div>
<div class="section" id="getdataasfloatarray">
<h3>getDataAsFloatArray<a class="headerlink" href="#getdataasfloatarray" title="Permalink to this headline">¶</a></h3>
<dl class="method">
<dt id="org.pytorch.Tensor.getDataAsFloatArray()">
public float[] <code class="sig-name descname">getDataAsFloatArray</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#org.pytorch.Tensor.getDataAsFloatArray()" title="Permalink to this definition">¶</a></dt>
<dd><dl class="field-list simple">
<dt class="field-odd">Throws</dt>
<dd class="field-odd"><ul class="simple">
<li><p><a class="reference external" href="http://docs.oracle.com/javase/8/docs/api/java/lang/IllegalStateException.html" title="java.lang.IllegalStateException"><strong>IllegalStateException</strong></a> – if it is called for a non-float32 tensor.</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>a Java float array that contains the tensor data. This may be a copy or reference.</p>
</dd>
</dl>
</dd></dl>

</div>
<div class="section" id="getdataasintarray">
<h3>getDataAsIntArray<a class="headerlink" href="#getdataasintarray" title="Permalink to this headline">¶</a></h3>
<dl class="method">
<dt id="org.pytorch.Tensor.getDataAsIntArray()">
public int[] <code class="sig-name descname">getDataAsIntArray</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#org.pytorch.Tensor.getDataAsIntArray()" title="Permalink to this definition">¶</a></dt>
<dd><dl class="field-list simple">
<dt class="field-odd">Throws</dt>
<dd class="field-odd"><ul class="simple">
<li><p><a class="reference external" href="http://docs.oracle.com/javase/8/docs/api/java/lang/IllegalStateException.html" title="java.lang.IllegalStateException"><strong>IllegalStateException</strong></a> – if it is called for a non-int32 tensor.</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>a Java int array that contains the tensor data. This may be a copy or reference.</p>
</dd>
</dl>
</dd></dl>

</div>
<div class="section" id="getdataaslongarray">
<h3>getDataAsLongArray<a class="headerlink" href="#getdataaslongarray" title="Permalink to this headline">¶</a></h3>
<dl class="method">
<dt id="org.pytorch.Tensor.getDataAsLongArray()">
public long[] <code class="sig-name descname">getDataAsLongArray</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#org.pytorch.Tensor.getDataAsLongArray()" title="Permalink to this definition">¶</a></dt>
<dd><dl class="field-list simple">
<dt class="field-odd">Throws</dt>
<dd class="field-odd"><ul class="simple">
<li><p><a class="reference external" href="http://docs.oracle.com/javase/8/docs/api/java/lang/IllegalStateException.html" title="java.lang.IllegalStateException"><strong>IllegalStateException</strong></a> – if it is called for a non-int64 tensor.</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>a Java long array that contains the tensor data. This may be a copy or reference.</p>
</dd>
</dl>
</dd></dl>

</div>
<div class="section" id="getdataasunsignedbytearray">
<h3>getDataAsUnsignedByteArray<a class="headerlink" href="#getdataasunsignedbytearray" title="Permalink to this headline">¶</a></h3>
<dl class="method">
<dt id="org.pytorch.Tensor.getDataAsUnsignedByteArray()">
public byte[] <code class="sig-name descname">getDataAsUnsignedByteArray</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#org.pytorch.Tensor.getDataAsUnsignedByteArray()" title="Permalink to this definition">¶</a></dt>
<dd><dl class="field-list simple">
<dt class="field-odd">Throws</dt>
<dd class="field-odd"><ul class="simple">
<li><p><a class="reference external" href="http://docs.oracle.com/javase/8/docs/api/java/lang/IllegalStateException.html" title="java.lang.IllegalStateException"><strong>IllegalStateException</strong></a> – if it is called for a non-uint8 tensor.</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>a Java byte array that contains the tensor data. This may be a copy or reference.</p>
</dd>
</dl>
</dd></dl>

</div>
<div class="section" id="getrawdatabuffer">
<h3>getRawDataBuffer<a class="headerlink" href="#getrawdatabuffer" title="Permalink to this headline">¶</a></h3>
<dl class="method">
<dt id="org.pytorch.Tensor.getRawDataBuffer()">
 <a class="reference external" href="http://docs.oracle.com/javase/8/docs/api/java/nio/Buffer.html" title="java.nio.Buffer">Buffer</a> <code class="sig-name descname">getRawDataBuffer</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#org.pytorch.Tensor.getRawDataBuffer()" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>

</div>
<div class="section" id="numel">
<h3>numel<a class="headerlink" href="#numel" title="Permalink to this headline">¶</a></h3>
<dl class="method">
<dt id="org.pytorch.Tensor.numel()">
public long <code class="sig-name descname">numel</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#org.pytorch.Tensor.numel()" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns the number of elements in this tensor.</p>
</dd></dl>

</div>
<div class="section" id="id11">
<h3>numel<a class="headerlink" href="#id11" title="Permalink to this headline">¶</a></h3>
<dl class="method">
<dt id="org.pytorch.Tensor.numel(long[])">
public static long <code class="sig-name descname">numel</code><span class="sig-paren">(</span>long[]<em> shape</em><span class="sig-paren">)</span><a class="headerlink" href="#org.pytorch.Tensor.numel(long[])" title="Permalink to this definition">¶</a></dt>
<dd><p>Calculates the number of elements in a tensor with the specified shape.</p>
</dd></dl>

</div>
<div class="section" id="shape">
<h3>shape<a class="headerlink" href="#shape" title="Permalink to this headline">¶</a></h3>
<dl class="method">
<dt id="org.pytorch.Tensor.shape()">
public long[] <code class="sig-name descname">shape</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#org.pytorch.Tensor.shape()" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns the shape of this tensor. (The array is a fresh copy.)</p>
</dd></dl>

</div>
</div>
</div>


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